Mr. Spencer H. Welland Profile

Spencer H. Welland

PhD Student at Ctr for Computer Vision and Imaging Biomarkers

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Publications (2)

Proceedings Article | 3 April 2024 Presentation + Paper

Assessing variability in non-contrast CT for the evaluation of stroke: the effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume

Spencer Welland, Grace Hyun Kim, Anil Yadav, John Hoffman, William Hsu, Matthew Brown, Elham Tavakkol, Kambiz Nael, Michael McNitt-Gray

Proceedings Volume 12927, 1292711 (2024) https://doi.org/10.1117/12.3006582

KEYWORDS: Computed tomography, CT reconstruction, Medical image reconstruction, Computer aided detection, Image restoration, Artificial intelligence, Visualization, Therapeutics, Ischemia, Neuroimaging

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Purpose: To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume. The purpose of this work was to investigate the effects of CT reconstruction kernel and slice thickness on ASPECTS and hypodense volume. Methods: The NCCT series image data of 87 patients imaged with a CT stroke protocol at our institution were reconstructed with 3 kernels (H10s-smooth, H40s-medium, H70h-sharp) and 2 slice thicknesses (1.5mm and 5mm) to create a reference condition (H40s/5mm) and 5 non-reference conditions. Each reconstruction for each patient was analyzed with the Brainomix e-Stroke software (Brainomix, Oxford, England) which yields an ASPECTS value and measure of total hypodense volume (mL). Results: An ASPECTS value was returned for 74 of 87 cases in the reference condition (13 failures). ASPECTS in non-reference conditions changed from that measured in the reference condition for 59 cases, 7 of which changed above or below the clinical threshold of 7 for 3 non-reference conditions. ANOVA tests were performed to compare the differences in protocols, Dunnett’s post-hoc tests were performed after ANOVA, and a significance level of p < 0.05 was defined. There was no significant effect of kernel (p = 0.91), a significant effect of slice thickness (p < 0.01) and no significant interaction between these factors (p = 0.91). Post-hoc tests indicated no significant difference between ASPECTS estimated in the reference and any non-reference conditions. There was a significant effect of kernel (p < 0.01) and slice thickness (p < 0.01) on hypodense volume, however there was no significant interaction between these factors (p = 0.79). Post-hoc tests indicated significantly different hypodense volume measurements for H10s/1.5mm (p = 0.03), H40s/1.5mm (p < 0.01), H70h/5mm (p < 0.01). No significant difference was found in hypodense volume measured in the H10s/5mm condition (p = 0.96). Conclusion: Automated ASPECTS and hypodense volume measurements can be significantly impacted by reconstruction kernel and slice thickness.

Proceedings Article | 3 April 2023 Poster + Paper

Using a GAN for CT contrast enhancement to improve CNN kidney segmentation accuracy

Spencer Welland, Gabriel Melendez-Corres, Pangyu Teng, Heidi Coy, Muhammad Wahi-Anwar, Steve Raman, Matthew Brown

Proceedings Volume 12464, 124642J (2023) https://doi.org/10.1117/12.2654038

KEYWORDS: Image segmentation, Kidney, Image contrast enhancement, Computed tomography, Visualization, Tumors, Medical imaging, Image quality

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